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   Zheng, Haizhong; Zhang, Ziqi; Gu, Juncheng; Lee, Honglak; Prakash, Atul (June 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

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   Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can re- quire orders of magnitude additional training time due to high cost of generating strong adversarial examples dur- ing training. In this paper, we first show that there is high transferability between models from neighboring epochs in the same training process, i.e., adversarial examples from one epoch continue to be adversarial in subsequent epochs. Leveraging this property, we propose a novel method, Adversarial Training with Transferable Adversarial Examples (ATTA), that can enhance the robustness of trained models and greatly improve the training efficiency by accumulating adversarial perturbations through epochs. Compared to state-of-the-art adversarial training methods, ATTA enhances adversarial accuracy by up to 7.2% on CIFAR10 and requires 12 ∼ 14× less training time on MNIST and CIFAR10 datasets with comparable model robustness. 

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   Shi, Chenghui; Xu, Xiaogang; Ji, Shouling; Bu, Kai; Chen, Jianhai; Beyah, Raheem; Wang, Ting (January 2021, IEEE Transactions on Cybernetics)

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4. Generalization Properties of Adversarial Training for l0 Bounded Adversarial Attacks

   https://doi.org/10.1109/ITW55543.2023.10161648  

   Delgosha, Payam; Hassani, Hamed; Pedarsani, Ramtin (April 2023, 2023 IEEE Information Theory Workshop (ITW))
5. Adversarial Aerial MetaSurfaces

   https://doi.org/10.1145/3572864.3581581  

   Shaikhanov, Zhambyl; Badran, Sherif; Jornet, Josep M.; Mittleman, Daniel M.; Knightly, Edward W. (February 2023, 24th International Workshop on Mobile Computing Systems and Applications)